Abstract
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occurrence of DCM2Ms due to the proximity with M3Ms and determine the relative importance of predictive variables for DCM2Ms that are important for clinical decision making. A total of 2642 mandibular second molars adjacent to M3Ms were analyzed and DCM2Ms were identified in 322 cases (12.2%). The models were trained using logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting ML methods and were subsequently validated using testing datasets. The performance of the ML models was significantly superior to that of single predictors. The area under the receiver operating characteristic curve of the machine learning models ranged from 0.88 to 0.89. Six features (sex, age, contact point at the cementoenamel junction, angulation of M3Ms, Winter's classification, and Pell and Gregory classification) were identified as relevant predictors. These prediction models could be used to detect patients at a high risk of developing DCM2M and ultimately contribute to caries prevention and treatment decision-making for impacted M3Ms.
Highlights
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M)
We developed five final machine learning (ML) models to predict DCM2Ms in the training dataset, by tuning the hyper-parameters using the caret package provided with the R statistical software
Correlation analysis revealed a slight correlation between the DCM2M-positive group and two variables, namely the contact point (ρ = 0.29, P < 0.001), and Pell and Gregory classification (ρ = −0.21, P < 0.001)
Summary
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). Six features (sex, age, contact point at the cementoenamel junction, angulation of M3Ms, Winter’s classification, and Pell and Gregory classification) were identified as relevant predictors These prediction models could be used to detect patients at a high risk of developing DCM2M and contribute to caries prevention and treatment decision-making for impacted M3Ms. Mandibular third molars (M3M) have the highest impaction rate of all teeth in the human d entition[1]. Considering the multifactorial nature of the development of DCM2M, a single predictive factor is insufficient to accurately predict its occurrence; various factors need to be considered together as a complex This perspective highlights the limitations of the traditional approach that analyzed each risk factor separately. This study was to develop and validate five ML models designed to predict DCM2Ms arising from the proximity to M3Ms to provide guidelines for clinical decision making
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